Abstract
Time shift among samples remains a significant challenge in data analysis, such as quality control of natural plant extracts and metabolic profiling analysis, because this phenomenon may lead to invalid conclusions. In this work, we propose a new time shift alignment method, namely, automatic time-shift alignment (ATSA), for complicated chromatographic data analysis. This technique comprised the following alignment stages: (1) automatic baseline correction and peak detection stage for providing useful chromatographic information; (2) preliminary alignment stage through adaptive segment partition to correct alignment for the entire chromatogram; and (3) precise alignment stage based on test chromatographic peak information to accurately align time shift. In ATSA, the chromatographic peak information of both reference and test samples can be completely employed for time-shift alignment to determine segment boundaries and avoid loss of information. ATSA was used to analyze a complicated chromatographic dataset. The obtained correlation coefficients among samples and data analysis efficiency indicated that the influences of time shift can be considerably reduced by ATSA; thus accurate conclusion could be obtained.
Highlights
Development of chromatographic fingerprint-based methods for quality control of natural plant extracts, such as essential oils, is important for medical and industrial applications
We proposed a peak alignment method for metabolic profiling analysis[27]
CAMMPA is unsuitable for quality control based on an entire chromatogram
Summary
To ensure that the test chromatogram keeps the same length as the reference chromatogram after preliminary alignment, we adopted a warping strategy based on linear interpolation, which compresses long segments and stretches short segments. Peak information in the test chromatogram, involving peak start and end position as well as retention time, will be modified . Linear interpolation was used to compress or stretch each sub-segment part independently In this precise alignment, the retention time of each peak will be precisely located after time-shift correction and the problem across samples can maximally corrected. These slightly shifted values could be accurately modified after precise alignment. The ideologies and advantages of COW were illustrated by Tomasi et al.[13, 35]
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